Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations346
Missing cells1045
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.5 KiB
Average record size in memory448.3 B

Variable types

Numeric15
DateTime4
Categorical21

Alerts

Signet_Ring has constant value "1.0"Constant
AJCC_Substage is highly overall correlated with LN_Positive and 2 other fieldsHigh correlation
CEA_PreOp is highly overall correlated with Log_CEA_PreOp and 1 other fieldsHigh correlation
Chart_No is highly overall correlated with Patient_IDHigh correlation
DFS_Months is highly overall correlated with Death and 2 other fieldsHigh correlation
Death is highly overall correlated with DFS_Months and 3 other fieldsHigh correlation
Death_Cause is highly overall correlated with Death and 1 other fieldsHigh correlation
Differentiation is highly overall correlated with Op_ProcedureHigh correlation
Dx_Year is highly overall correlated with OS_Months and 1 other fieldsHigh correlation
Histology is highly overall correlated with Mucinous_Any and 1 other fieldsHigh correlation
LNR is highly overall correlated with LN_Positive and 1 other fieldsHigh correlation
LN_Positive is highly overall correlated with AJCC_Substage and 2 other fieldsHigh correlation
Log_CEA_PreOp is highly overall correlated with CEA_PreOp and 2 other fieldsHigh correlation
Mucinous_Any is highly overall correlated with Histology and 1 other fieldsHigh correlation
Mucinous_Gt_50 is highly overall correlated with Histology and 1 other fieldsHigh correlation
OS_Months is highly overall correlated with DFS_Months and 2 other fieldsHigh correlation
Op_Procedure is highly overall correlated with Differentiation and 2 other fieldsHigh correlation
Patient_ID is highly overall correlated with Chart_No and 1 other fieldsHigh correlation
Recurrence is highly overall correlated with DFS_Months and 4 other fieldsHigh correlation
Recurrence_Type is highly overall correlated with CEA_PreOp and 2 other fieldsHigh correlation
Tumor_Deposits is highly overall correlated with pN_StageHigh correlation
Tumor_Location is highly overall correlated with Op_Procedure and 1 other fieldsHigh correlation
Tumor_Location_Group is highly overall correlated with Op_Procedure and 1 other fieldsHigh correlation
pN_Stage is highly overall correlated with AJCC_Substage and 3 other fieldsHigh correlation
pT_Stage is highly overall correlated with AJCC_SubstageHigh correlation
Histology is highly imbalanced (73.3%)Imbalance
Differentiation is highly imbalanced (74.6%)Imbalance
Tumor_Deposits is highly imbalanced (69.3%)Imbalance
Mucinous_Gt_50 is highly imbalanced (69.3%)Imbalance
Mucinous_Any is highly imbalanced (53.7%)Imbalance
MSI_Status is highly imbalanced (61.4%)Imbalance
Recurrence_Type is highly imbalanced (56.1%)Imbalance
BMI has 4 (1.2%) missing valuesMissing
ECOG has 18 (5.2%) missing valuesMissing
pN_Stage has 83 (24.0%) missing valuesMissing
LVI has 4 (1.2%) missing valuesMissing
Signet_Ring has 344 (99.4%) missing valuesMissing
CEA_PreOp has 6 (1.7%) missing valuesMissing
Log_CEA_PreOp has 6 (1.7%) missing valuesMissing
PreOp_Albumin has 57 (16.5%) missing valuesMissing
Recurrence_Date has 258 (74.6%) missing valuesMissing
Recurrence_Type has 258 (74.6%) missing valuesMissing
Patient_ID is uniformly distributedUniform
Patient_ID has unique valuesUnique
Chart_No has unique valuesUnique
LN_Positive has 19 (5.5%) zerosZeros
LNR has 19 (5.5%) zerosZeros

Reproduction

Analysis started2025-11-02 16:21:04.129599
Analysis finished2025-11-02 16:21:09.616421
Duration5.49 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Patient_ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.5
Minimum1
Maximum346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:09.635733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.25
Q187.25
median173.5
Q3259.75
95-th percentile328.75
Maximum346
Range345
Interquartile range (IQR)172.5

Descriptive statistics

Standard deviation100.02583
Coefficient of variation (CV)0.57651775
Kurtosis-1.2
Mean173.5
Median Absolute Deviation (MAD)86.5
Skewness0
Sum60031
Variance10005.167
MonotonicityStrictly increasing
2025-11-03T00:21:09.667110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.3%
2281
 
0.3%
2361
 
0.3%
2351
 
0.3%
2341
 
0.3%
2331
 
0.3%
2321
 
0.3%
2311
 
0.3%
2301
 
0.3%
2291
 
0.3%
Other values (336)336
97.1%
ValueCountFrequency (%)
11
0.3%
21
0.3%
31
0.3%
41
0.3%
51
0.3%
61
0.3%
71
0.3%
81
0.3%
91
0.3%
101
0.3%
ValueCountFrequency (%)
3461
0.3%
3451
0.3%
3441
0.3%
3431
0.3%
3421
0.3%
3411
0.3%
3401
0.3%
3391
0.3%
3381
0.3%
3371
0.3%

Chart_No
Real number (ℝ)

High correlation  Unique 

Distinct346
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12796041
Minimum170832
Maximum19350595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:09.695586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum170832
5-th percentile1476152.8
Q19185287.5
median14975276
Q317422205
95-th percentile18510017
Maximum19350595
Range19179763
Interquartile range (IQR)8236917.2

Descriptive statistics

Standard deviation5711977.8
Coefficient of variation (CV)0.44638634
Kurtosis-0.6133524
Mean12796041
Median Absolute Deviation (MAD)2951235.5
Skewness-0.8577656
Sum4.4274301 × 109
Variance3.2626691 × 1013
MonotonicityStrictly increasing
2025-11-03T00:21:09.726195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1708321
 
0.3%
168020431
 
0.3%
169816161
 
0.3%
169629171
 
0.3%
169343861
 
0.3%
169262311
 
0.3%
168432461
 
0.3%
168295971
 
0.3%
168255061
 
0.3%
168136751
 
0.3%
Other values (336)336
97.1%
ValueCountFrequency (%)
1708321
0.3%
1907831
0.3%
3356151
0.3%
4581731
0.3%
5367101
0.3%
5456201
0.3%
6575891
0.3%
7068651
0.3%
7900781
0.3%
8263021
0.3%
ValueCountFrequency (%)
193505951
0.3%
193322421
0.3%
192778281
0.3%
192449631
0.3%
192344251
0.3%
192197061
0.3%
191618211
0.3%
191273341
0.3%
191148861
0.3%
190705101
0.3%

Dx_Date
Date

Distinct304
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2017-01-14 00:00:00
Maximum2022-01-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-03T00:21:09.752980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:09.782532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Dx_Year
Categorical

High correlation 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
2021
80 
2020
78 
2019
76 
2018
63 
2017
49 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1384
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2021
4th row2021
5th row2020

Common Values

ValueCountFrequency (%)
202180
23.1%
202078
22.5%
201976
22.0%
201863
18.2%
201749
14.2%

Length

2025-11-03T00:21:09.809407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:09.825719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
202180
23.1%
202078
22.5%
201976
22.0%
201863
18.2%
201749
14.2%

Most occurring characters

ValueCountFrequency (%)
2504
36.4%
0424
30.6%
1268
19.4%
976
 
5.5%
863
 
4.6%
749
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2504
36.4%
0424
30.6%
1268
19.4%
976
 
5.5%
863
 
4.6%
749
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2504
36.4%
0424
30.6%
1268
19.4%
976
 
5.5%
863
 
4.6%
749
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2504
36.4%
0424
30.6%
1268
19.4%
976
 
5.5%
863
 
4.6%
749
 
3.5%

Age
Real number (ℝ)

Distinct61
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.419075
Minimum23
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:09.853636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile44
Q156
median66
Q376
95-th percentile85
Maximum98
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.172021
Coefficient of variation (CV)0.20134832
Kurtosis-0.42762385
Mean65.419075
Median Absolute Deviation (MAD)10
Skewness-0.15286429
Sum22635
Variance173.50213
MonotonicityNot monotonic
2025-11-03T00:21:09.880982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6214
 
4.0%
7012
 
3.5%
6711
 
3.2%
8111
 
3.2%
7811
 
3.2%
6411
 
3.2%
5011
 
3.2%
6810
 
2.9%
6610
 
2.9%
6010
 
2.9%
Other values (51)235
67.9%
ValueCountFrequency (%)
231
 
0.3%
311
 
0.3%
321
 
0.3%
361
 
0.3%
371
 
0.3%
382
 
0.6%
391
 
0.3%
411
 
0.3%
426
1.7%
432
 
0.6%
ValueCountFrequency (%)
981
 
0.3%
941
 
0.3%
921
 
0.3%
911
 
0.3%
902
 
0.6%
892
 
0.6%
885
1.4%
872
 
0.6%
862
 
0.6%
856
1.7%

Sex
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
1
199 
2
147 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1199
57.5%
2147
42.5%

Length

2025-11-03T00:21:09.905510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:09.918348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1199
57.5%
2147
42.5%

Most occurring characters

ValueCountFrequency (%)
1199
57.5%
2147
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1199
57.5%
2147
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1199
57.5%
2147
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1199
57.5%
2147
42.5%

BMI
Real number (ℝ)

Missing 

Distinct304
Distinct (%)88.9%
Missing4
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean23.861725
Minimum0
Maximum60.61
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:09.936804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.142
Q120.7725
median23.375
Q326.4925
95-th percentile31.7295
Maximum60.61
Range60.61
Interquartile range (IQR)5.72

Descriptive statistics

Standard deviation5.0006798
Coefficient of variation (CV)0.20956908
Kurtosis9.3192761
Mean23.861725
Median Absolute Deviation (MAD)2.855
Skewness1.2011512
Sum8160.71
Variance25.006798
MonotonicityNot monotonic
2025-11-03T00:21:09.963595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.393
 
0.9%
20.173
 
0.9%
23.033
 
0.9%
20.652
 
0.6%
23.162
 
0.6%
21.912
 
0.6%
22.072
 
0.6%
28.412
 
0.6%
21.542
 
0.6%
26.942
 
0.6%
Other values (294)319
92.2%
(Missing)4
 
1.2%
ValueCountFrequency (%)
01
0.3%
13.791
0.3%
14.151
0.3%
14.181
0.3%
14.891
0.3%
15.021
0.3%
15.061
0.3%
15.221
0.3%
15.421
0.3%
15.481
0.3%
ValueCountFrequency (%)
60.611
0.3%
391
0.3%
38.321
0.3%
36.271
0.3%
36.251
0.3%
35.381
0.3%
35.361
0.3%
34.721
0.3%
34.281
0.3%
34.211
0.3%

ECOG
Categorical

Missing 

Distinct4
Distinct (%)1.2%
Missing18
Missing (%)5.2%
Memory size17.8 KiB
1.0
214 
0.0
84 
2.0
22 
3.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters984
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0214
61.8%
0.084
 
24.3%
2.022
 
6.4%
3.08
 
2.3%
(Missing)18
 
5.2%

Length

2025-11-03T00:21:09.990446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.004970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0214
65.2%
0.084
 
25.6%
2.022
 
6.7%
3.08
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0412
41.9%
.328
33.3%
1214
21.7%
222
 
2.2%
38
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0412
41.9%
.328
33.3%
1214
21.7%
222
 
2.2%
38
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0412
41.9%
.328
33.3%
1214
21.7%
222
 
2.2%
38
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0412
41.9%
.328
33.3%
1214
21.7%
222
 
2.2%
38
 
0.8%

Tumor_Location
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5231214
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.019759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4603022
Coefficient of variation (CV)0.44545503
Kurtosis-1.1752129
Mean5.5231214
Median Absolute Deviation (MAD)1
Skewness-0.70981314
Sum1911
Variance6.053087
MonotonicityNot monotonic
2025-11-03T00:21:10.037744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7139
40.2%
868
19.7%
266
19.1%
122
 
6.4%
421
 
6.1%
620
 
5.8%
37
 
2.0%
53
 
0.9%
ValueCountFrequency (%)
122
 
6.4%
266
19.1%
37
 
2.0%
421
 
6.1%
53
 
0.9%
620
 
5.8%
7139
40.2%
868
19.7%
ValueCountFrequency (%)
868
19.7%
7139
40.2%
620
 
5.8%
53
 
0.9%
421
 
6.1%
37
 
2.0%
266
19.1%
122
 
6.4%

Tumor_Location_Group
Categorical

High correlation 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
2
227 
1
119 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2227
65.6%
1119
34.4%

Length

2025-11-03T00:21:10.059083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.072033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2227
65.6%
1119
34.4%

Most occurring characters

ValueCountFrequency (%)
2227
65.6%
1119
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2227
65.6%
1119
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2227
65.6%
1119
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2227
65.6%
1119
34.4%

pT_Stage
Categorical

High correlation 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size902.0 B
3
243 
4A
44 
2
30 
1
 
15
4B
 
14

Length

Max length2
Median length1
Mean length1.1676301
Min length1

Characters and Unicode

Total characters404
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4A
2nd row3
3rd row3
4th row4A
5th row3

Common Values

ValueCountFrequency (%)
3243
70.2%
4A44
 
12.7%
230
 
8.7%
115
 
4.3%
4B14
 
4.0%

Length

2025-11-03T00:21:10.088716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.107352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3243
70.2%
4a44
 
12.7%
230
 
8.7%
115
 
4.3%
4b14
 
4.0%

Most occurring characters

ValueCountFrequency (%)
3243
60.1%
458
 
14.4%
A44
 
10.9%
230
 
7.4%
115
 
3.7%
B14
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3243
60.1%
458
 
14.4%
A44
 
10.9%
230
 
7.4%
115
 
3.7%
B14
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3243
60.1%
458
 
14.4%
A44
 
10.9%
230
 
7.4%
115
 
3.7%
B14
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3243
60.1%
458
 
14.4%
A44
 
10.9%
230
 
7.4%
115
 
3.7%
B14
 
3.5%

pN_Stage
Categorical

High correlation  Missing 

Distinct3
Distinct (%)1.1%
Missing83
Missing (%)24.0%
Memory size856.0 B
1B
109 
1A
97 
2B
57 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters526
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2B
2nd row1B
3rd row1B
4th row1B
5th row1B

Common Values

ValueCountFrequency (%)
1B109
31.5%
1A97
28.0%
2B57
16.5%
(Missing)83
24.0%

Length

2025-11-03T00:21:10.126703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.140114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1b109
41.4%
1a97
36.9%
2b57
21.7%

Most occurring characters

ValueCountFrequency (%)
1206
39.2%
B166
31.6%
A97
18.4%
257
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1206
39.2%
B166
31.6%
A97
18.4%
257
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1206
39.2%
B166
31.6%
A97
18.4%
257
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1206
39.2%
B166
31.6%
A97
18.4%
257
 
10.8%

AJCC_Substage
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size739.0 B
3B
237 
3C
69 
3A
40 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters692
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3C
2nd row3B
3rd row3B
4th row3B
5th row3B

Common Values

ValueCountFrequency (%)
3B237
68.5%
3C69
 
19.9%
3A40
 
11.6%

Length

2025-11-03T00:21:10.158601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.172687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3b237
68.5%
3c69
 
19.9%
3a40
 
11.6%

Most occurring characters

ValueCountFrequency (%)
3346
50.0%
B237
34.2%
C69
 
10.0%
A40
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3346
50.0%
B237
34.2%
C69
 
10.0%
A40
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3346
50.0%
B237
34.2%
C69
 
10.0%
A40
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3346
50.0%
B237
34.2%
C69
 
10.0%
A40
 
5.8%

LN_Total
Real number (ℝ)

Distinct43
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.291908
Minimum9
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.191292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12
Q117
median21
Q328
95-th percentile40
Maximum65
Range56
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.1666562
Coefficient of variation (CV)0.39355541
Kurtosis2.7172921
Mean23.291908
Median Absolute Deviation (MAD)5
Skewness1.402984
Sum8059
Variance84.027586
MonotonicityNot monotonic
2025-11-03T00:21:10.216465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1922
 
6.4%
1622
 
6.4%
2221
 
6.1%
1221
 
6.1%
1721
 
6.1%
2617
 
4.9%
2017
 
4.9%
2117
 
4.9%
2316
 
4.6%
1815
 
4.3%
Other values (33)157
45.4%
ValueCountFrequency (%)
91
 
0.3%
1221
6.1%
1312
3.5%
1413
3.8%
1514
4.0%
1622
6.4%
1721
6.1%
1815
4.3%
1922
6.4%
2017
4.9%
ValueCountFrequency (%)
651
 
0.3%
641
 
0.3%
543
0.9%
511
 
0.3%
501
 
0.3%
491
 
0.3%
482
0.6%
471
 
0.3%
451
 
0.3%
441
 
0.3%

LN_Positive
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6589595
Minimum0
Maximum32
Zeros19
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.240900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2.5
Q35
95-th percentile11
Maximum32
Range32
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7485291
Coefficient of variation (CV)1.0244795
Kurtosis11.369561
Mean3.6589595
Median Absolute Deviation (MAD)1.5
Skewness2.6127072
Sum1266
Variance14.05147
MonotonicityNot monotonic
2025-11-03T00:21:10.260135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
196
27.7%
258
16.8%
352
15.0%
429
 
8.4%
019
 
5.5%
519
 
5.5%
616
 
4.6%
715
 
4.3%
811
 
3.2%
106
 
1.7%
Other values (8)25
 
7.2%
ValueCountFrequency (%)
019
 
5.5%
196
27.7%
258
16.8%
352
15.0%
429
 
8.4%
519
 
5.5%
616
 
4.6%
715
 
4.3%
811
 
3.2%
94
 
1.2%
ValueCountFrequency (%)
321
 
0.3%
202
 
0.6%
162
 
0.6%
144
 
1.2%
132
 
0.6%
126
1.7%
114
 
1.2%
106
1.7%
94
 
1.2%
811
3.2%

LNR
Real number (ℝ)

High correlation  Zeros 

Distinct134
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16873022
Minimum0
Maximum0.92307692
Zeros19
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.284678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.055555556
median0.11324786
Q30.21428571
95-th percentile0.51973684
Maximum0.92307692
Range0.92307692
Interquartile range (IQR)0.15873016

Descriptive statistics

Standard deviation0.16402123
Coefficient of variation (CV)0.97209163
Kurtosis3.1422627
Mean0.16873022
Median Absolute Deviation (MAD)0.067793318
Skewness1.7454062
Sum58.380656
Variance0.026902965
MonotonicityNot monotonic
2025-11-03T00:21:10.310933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
5.5%
0.0714285714312
 
3.5%
0.2510
 
2.9%
0.16666666679
 
2.6%
0.058823529419
 
2.6%
0.052631578958
 
2.3%
0.083333333337
 
2.0%
0.14285714297
 
2.0%
0.13636363647
 
2.0%
0.17
 
2.0%
Other values (124)251
72.5%
ValueCountFrequency (%)
019
5.5%
0.019607843141
 
0.3%
0.020408163271
 
0.3%
0.021276595741
 
0.3%
0.022727272731
 
0.3%
0.0251
 
0.3%
0.028571428572
 
0.6%
0.029411764712
 
0.6%
0.03030303032
 
0.6%
0.031254
 
1.2%
ValueCountFrequency (%)
0.92307692311
0.3%
0.81
0.3%
0.77777777781
0.3%
0.76470588241
0.3%
0.72727272731
0.3%
0.70588235291
0.3%
0.66666666671
0.3%
0.64705882351
0.3%
0.63157894741
0.3%
0.6251
0.3%

Histology
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
1
320 
2
 
23
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1320
92.5%
223
 
6.6%
33
 
0.9%

Length

2025-11-03T00:21:10.333985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.350410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1320
92.5%
223
 
6.6%
33
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1320
92.5%
223
 
6.6%
33
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1320
92.5%
223
 
6.6%
33
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1320
92.5%
223
 
6.6%
33
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1320
92.5%
223
 
6.6%
33
 
0.9%

Differentiation
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
2
312 
3
 
24
4
 
5
1
 
4
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row3
2nd row2
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
2312
90.2%
324
 
6.9%
45
 
1.4%
14
 
1.2%
91
 
0.3%

Length

2025-11-03T00:21:10.367150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.382076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2312
90.2%
324
 
6.9%
45
 
1.4%
14
 
1.2%
91
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2312
90.2%
324
 
6.9%
45
 
1.4%
14
 
1.2%
91
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2312
90.2%
324
 
6.9%
45
 
1.4%
14
 
1.2%
91
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2312
90.2%
324
 
6.9%
45
 
1.4%
14
 
1.2%
91
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2312
90.2%
324
 
6.9%
45
 
1.4%
14
 
1.2%
91
 
0.3%

LVI
Categorical

Missing 

Distinct2
Distinct (%)0.6%
Missing4
Missing (%)1.2%
Memory size17.7 KiB
1.0
174 
0.0
168 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1026
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0174
50.3%
0.0168
48.6%
(Missing)4
 
1.2%

Length

2025-11-03T00:21:10.400743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.413945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0174
50.9%
0.0168
49.1%

Most occurring characters

ValueCountFrequency (%)
0510
49.7%
.342
33.3%
1174
 
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0510
49.7%
.342
33.3%
1174
 
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0510
49.7%
.342
33.3%
1174
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0510
49.7%
.342
33.3%
1174
 
17.0%

PNI
Categorical

Distinct2
Distinct (%)0.6%
Missing3
Missing (%)0.9%
Memory size17.7 KiB
0.0
288 
1.0
55 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1029
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0288
83.2%
1.055
 
15.9%
(Missing)3
 
0.9%

Length

2025-11-03T00:21:10.430935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.444198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0288
84.0%
1.055
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0631
61.3%
.343
33.3%
155
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0631
61.3%
.343
33.3%
155
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0631
61.3%
.343
33.3%
155
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0631
61.3%
.343
33.3%
155
 
5.3%

Tumor_Deposits
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
327 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Length

2025-11-03T00:21:10.461123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.477320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Mucinous_Gt_50
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
327 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Length

2025-11-03T00:21:10.493431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.506234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0327
94.5%
119
 
5.5%

Mucinous_Any
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
312 
1
34 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0312
90.2%
134
 
9.8%

Length

2025-11-03T00:21:10.522163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.534999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0312
90.2%
134
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0312
90.2%
134
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0312
90.2%
134
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0312
90.2%
134
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0312
90.2%
134
 
9.8%

Signet_Ring
Categorical

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing344
Missing (%)99.4%
Memory size19.0 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0

Common Values

ValueCountFrequency (%)
1.02
 
0.6%
(Missing)344
99.4%

Length

2025-11-03T00:21:10.550338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.561687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.02
100.0%

Most occurring characters

ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

MSI_Status
Categorical

Imbalance 

Distinct2
Distinct (%)0.6%
Missing2
Missing (%)0.6%
Memory size17.8 KiB
MSS
318 
MSI-H
 
26

Length

Max length5
Median length3
Mean length3.1511628
Min length3

Characters and Unicode

Total characters1084
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMSS
2nd rowMSS
3rd rowMSS
4th rowMSI-H
5th rowMSS

Common Values

ValueCountFrequency (%)
MSS318
91.9%
MSI-H26
 
7.5%
(Missing)2
 
0.6%

Length

2025-11-03T00:21:10.578392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.592796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mss318
92.4%
msi-h26
 
7.6%

Most occurring characters

ValueCountFrequency (%)
S662
61.1%
M344
31.7%
I26
 
2.4%
-26
 
2.4%
H26
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S662
61.1%
M344
31.7%
I26
 
2.4%
-26
 
2.4%
H26
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S662
61.1%
M344
31.7%
I26
 
2.4%
-26
 
2.4%
H26
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S662
61.1%
M344
31.7%
I26
 
2.4%
-26
 
2.4%
H26
 
2.4%

Tumor_Size_cm
Real number (ℝ)

Distinct84
Distinct (%)24.4%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4.6331395
Minimum0.1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.614045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.815
Q13.2
median4.3
Q35.525
95-th percentile8.785
Maximum15
Range14.9
Interquartile range (IQR)2.325

Descriptive statistics

Standard deviation2.2059568
Coefficient of variation (CV)0.4761257
Kurtosis2.0694149
Mean4.6331395
Median Absolute Deviation (MAD)1.2
Skewness1.113498
Sum1593.8
Variance4.8662455
MonotonicityNot monotonic
2025-11-03T00:21:10.641435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
418
 
5.2%
4.517
 
4.9%
3.516
 
4.6%
313
 
3.8%
4.710
 
2.9%
5.510
 
2.9%
6.510
 
2.9%
4.310
 
2.9%
3.410
 
2.9%
3.29
 
2.6%
Other values (74)221
63.9%
ValueCountFrequency (%)
0.11
 
0.3%
0.21
 
0.3%
0.31
 
0.3%
0.51
 
0.3%
0.92
0.6%
13
0.9%
1.11
 
0.3%
1.52
0.6%
1.62
0.6%
1.84
1.2%
ValueCountFrequency (%)
151
 
0.3%
12.51
 
0.3%
121
 
0.3%
11.81
 
0.3%
11.51
 
0.3%
10.71
 
0.3%
10.51
 
0.3%
104
1.2%
9.81
 
0.3%
9.51
 
0.3%

CEA_PreOp
Real number (ℝ)

High correlation  Missing 

Distinct132
Distinct (%)38.8%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean23.872206
Minimum0.5
Maximum3443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.668405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q11.7
median2.95
Q37.925
95-th percentile44.205
Maximum3443
Range3442.5
Interquartile range (IQR)6.225

Descriptive statistics

Standard deviation195.14382
Coefficient of variation (CV)8.1745197
Kurtosis281.32307
Mean23.872206
Median Absolute Deviation (MAD)1.75
Skewness16.281858
Sum8116.55
Variance38081.109
MonotonicityNot monotonic
2025-11-03T00:21:10.696114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.528
 
8.1%
2.511
 
3.2%
1.111
 
3.2%
1.310
 
2.9%
2.110
 
2.9%
1.510
 
2.9%
2.99
 
2.6%
1.99
 
2.6%
3.48
 
2.3%
1.78
 
2.3%
Other values (122)226
65.3%
ValueCountFrequency (%)
0.528
8.1%
17
 
2.0%
1.111
 
3.2%
1.23
 
0.9%
1.310
 
2.9%
1.48
 
2.3%
1.510
 
2.9%
1.62
 
0.6%
1.78
 
2.3%
1.711
 
0.3%
ValueCountFrequency (%)
34431
0.3%
914.21
0.3%
4711
0.3%
181.71
0.3%
158.51
0.3%
142.11
0.3%
134.62
0.6%
128.51
0.3%
79.611
0.3%
711
0.3%

Log_CEA_PreOp
Real number (ℝ)

High correlation  Missing 

Distinct132
Distinct (%)38.8%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean1.7241891
Minimum0.40546511
Maximum8.1443889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.725723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.40546511
5-th percentile0.40546511
Q10.99325177
median1.3736355
Q32.1888446
95-th percentile3.8112076
Maximum8.1443889
Range7.7389238
Interquartile range (IQR)1.1955928

Descriptive statistics

Standard deviation1.1111104
Coefficient of variation (CV)0.64442489
Kurtosis4.9686681
Mean1.7241891
Median Absolute Deviation (MAD)0.51343419
Skewness1.7840414
Sum586.22428
Variance1.2345662
MonotonicityNot monotonic
2025-11-03T00:21:10.753636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.405465108128
 
8.1%
1.25276296811
 
3.2%
0.741937344711
 
3.2%
0.832909122910
 
2.9%
1.13140211110
 
2.9%
0.916290731910
 
2.9%
1.3609765539
 
2.6%
1.0647107379
 
2.6%
1.4816045418
 
2.3%
0.9932517738
 
2.3%
Other values (122)226
65.3%
ValueCountFrequency (%)
0.405465108128
8.1%
0.69314718067
 
2.0%
0.741937344711
 
3.2%
0.78845736043
 
0.9%
0.832909122910
 
2.9%
0.87546873748
 
2.3%
0.916290731910
 
2.9%
0.9555114452
 
0.6%
0.9932517738
 
2.3%
0.99694863491
 
0.3%
ValueCountFrequency (%)
8.1443888661
0.3%
6.8191426211
0.3%
6.1569789861
0.3%
5.2078454631
0.3%
5.0720439221
0.3%
4.9635436871
0.3%
4.9097093762
0.6%
4.8636808811
0.3%
4.3896227111
0.3%
4.2766661191
0.3%
Distinct307
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2017-01-23 00:00:00
Maximum2022-02-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-03T00:21:10.781081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:10.811407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Op_Procedure
Categorical

High correlation 

Distinct13
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
Laparoscopic anterior resection
126 
Laparoscopic right hemicolectomy
70 
Laparoscopic low anterior resection
47 
Laparoscopic left hemicolectomy
26 
Anterior resection
24 
Other values (8)
53 

Length

Max length62
Median length41
Mean length30.557803
Min length18

Characters and Unicode

Total characters10573
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st rowLaparoscopic right hemicolectomy
2nd rowAnterior resection
3rd rowLaparoscopic anterior resection
4th rowLaparoscopic right hemicolectomy
5th rowLaparoscopic anterior resection

Common Values

ValueCountFrequency (%)
Laparoscopic anterior resection126
36.4%
Laparoscopic right hemicolectomy70
20.2%
Laparoscopic low anterior resection47
 
13.6%
Laparoscopic left hemicolectomy26
 
7.5%
Anterior resection24
 
6.9%
Right hemicolectomy20
 
5.8%
Laparoscopic extended right hemicolectomy12
 
3.5%
Low anterior resection11
 
3.2%
Single Incision Laparoscopic Surgery (SILS) anterior resection4
 
1.2%
Extended right hemicolectomy3
 
0.9%
Other values (3)3
 
0.9%

Length

2025-11-03T00:21:10.841872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
laparoscopic287
26.8%
anterior212
19.8%
resection212
19.8%
hemicolectomy133
12.4%
right106
 
9.9%
low58
 
5.4%
left28
 
2.6%
extended17
 
1.6%
single4
 
0.4%
incision4
 
0.4%
Other values (3)9
 
0.8%

Most occurring characters

ValueCountFrequency (%)
o1328
12.6%
c1058
10.0%
r1017
9.6%
e987
9.3%
i963
9.1%
a762
 
7.2%
724
 
6.8%
t709
 
6.7%
p574
 
5.4%
s503
 
4.8%
Other values (19)1948
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o1328
12.6%
c1058
10.0%
r1017
9.6%
e987
9.3%
i963
9.1%
a762
 
7.2%
724
 
6.8%
t709
 
6.7%
p574
 
5.4%
s503
 
4.8%
Other values (19)1948
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o1328
12.6%
c1058
10.0%
r1017
9.6%
e987
9.3%
i963
9.1%
a762
 
7.2%
724
 
6.8%
t709
 
6.7%
p574
 
5.4%
s503
 
4.8%
Other values (19)1948
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o1328
12.6%
c1058
10.0%
r1017
9.6%
e987
9.3%
i963
9.1%
a762
 
7.2%
724
 
6.8%
t709
 
6.7%
p574
 
5.4%
s503
 
4.8%
Other values (19)1948
18.4%

PreOp_Albumin
Real number (ℝ)

Missing 

Distinct29
Distinct (%)10.0%
Missing57
Missing (%)16.5%
Infinite0
Infinite (%)0.0%
Mean3.9176471
Minimum2.1
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:10.860821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile2.8
Q13.6
median4
Q34.3
95-th percentile4.6
Maximum4.9
Range2.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.56137233
Coefficient of variation (CV)0.14329324
Kurtosis0.74058011
Mean3.9176471
Median Absolute Deviation (MAD)0.3
Skewness-0.97980087
Sum1132.2
Variance0.31513889
MonotonicityNot monotonic
2025-11-03T00:21:10.883086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
429
 
8.4%
4.227
 
7.8%
4.124
 
6.9%
4.324
 
6.9%
4.424
 
6.9%
3.921
 
6.1%
4.516
 
4.6%
3.815
 
4.3%
3.513
 
3.8%
4.611
 
3.2%
Other values (19)85
24.6%
(Missing)57
16.5%
ValueCountFrequency (%)
2.12
0.6%
2.21
 
0.3%
2.32
0.6%
2.42
0.6%
2.52
0.6%
2.61
 
0.3%
2.73
0.9%
2.84
1.2%
2.94
1.2%
34
1.2%
ValueCountFrequency (%)
4.91
 
0.3%
4.86
 
1.7%
4.76
 
1.7%
4.611
 
3.2%
4.516
4.6%
4.424
6.9%
4.324
6.9%
4.227
7.8%
4.124
6.9%
429
8.4%
Distinct227
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2017-10-21 00:00:00
Maximum2024-05-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-03T00:21:10.908787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:10.937005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recurrence
Categorical

High correlation 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
258 
1
88 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0258
74.6%
188
 
25.4%

Length

2025-11-03T00:21:10.964573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:10.978096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0258
74.6%
188
 
25.4%

Most occurring characters

ValueCountFrequency (%)
0258
74.6%
188
 
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0258
74.6%
188
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0258
74.6%
188
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0258
74.6%
188
 
25.4%

Recurrence_Date
Date

Missing 

Distinct87
Distinct (%)98.9%
Missing258
Missing (%)74.6%
Memory size2.8 KiB
Minimum2017-10-17 00:00:00
Maximum2023-06-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-03T00:21:10.997354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:11.026691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recurrence_Type
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)2.3%
Missing258
Missing (%)74.6%
Memory size19.1 KiB
Distant
80 
Locoregional
 
8

Length

Max length12
Median length7
Mean length7.4545455
Min length7

Characters and Unicode

Total characters656
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLocoregional
2nd rowDistant
3rd rowDistant
4th rowDistant
5th rowDistant

Common Values

ValueCountFrequency (%)
Distant80
 
23.1%
Locoregional8
 
2.3%
(Missing)258
74.6%

Length

2025-11-03T00:21:11.052914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:11.065470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
distant80
90.9%
locoregional8
 
9.1%

Most occurring characters

ValueCountFrequency (%)
t160
24.4%
i88
13.4%
a88
13.4%
n88
13.4%
D80
12.2%
s80
12.2%
o24
 
3.7%
L8
 
1.2%
c8
 
1.2%
r8
 
1.2%
Other values (3)24
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t160
24.4%
i88
13.4%
a88
13.4%
n88
13.4%
D80
12.2%
s80
12.2%
o24
 
3.7%
L8
 
1.2%
c8
 
1.2%
r8
 
1.2%
Other values (3)24
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t160
24.4%
i88
13.4%
a88
13.4%
n88
13.4%
D80
12.2%
s80
12.2%
o24
 
3.7%
L8
 
1.2%
c8
 
1.2%
r8
 
1.2%
Other values (3)24
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t160
24.4%
i88
13.4%
a88
13.4%
n88
13.4%
D80
12.2%
s80
12.2%
o24
 
3.7%
L8
 
1.2%
c8
 
1.2%
r8
 
1.2%
Other values (3)24
 
3.7%

Death
Categorical

High correlation 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
236 
1
110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0236
68.2%
1110
31.8%

Length

2025-11-03T00:21:11.086022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:11.099221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0236
68.2%
1110
31.8%

Most occurring characters

ValueCountFrequency (%)
0236
68.2%
1110
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0236
68.2%
1110
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0236
68.2%
1110
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0236
68.2%
1110
31.8%

Death_Cause
Categorical

High correlation 

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
0
235 
1
71 
3
 
23
9
 
13
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters346
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0235
67.9%
171
 
20.5%
323
 
6.6%
913
 
3.8%
24
 
1.2%

Length

2025-11-03T00:21:11.116179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-03T00:21:11.131611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0235
67.9%
171
 
20.5%
323
 
6.6%
913
 
3.8%
24
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0235
67.9%
171
 
20.5%
323
 
6.6%
913
 
3.8%
24
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0235
67.9%
171
 
20.5%
323
 
6.6%
913
 
3.8%
24
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0235
67.9%
171
 
20.5%
323
 
6.6%
913
 
3.8%
24
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0235
67.9%
171
 
20.5%
323
 
6.6%
913
 
3.8%
24
 
1.2%

DFS_Months
Real number (ℝ)

High correlation 

Distinct326
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.992842
Minimum0.53
Maximum87.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:11.153193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.53
5-th percentile4.285
Q116.108333
median35.38
Q352.3525
95-th percentile74.1325
Maximum87.37
Range86.84
Interquartile range (IQR)36.244167

Descriptive statistics

Standard deviation22.358127
Coefficient of variation (CV)0.62118259
Kurtosis-0.87069922
Mean35.992842
Median Absolute Deviation (MAD)18.701667
Skewness0.22572253
Sum12453.523
Variance499.88584
MonotonicityNot monotonic
2025-11-03T00:21:11.179967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.733
 
0.9%
42.272
 
0.6%
2.1333333332
 
0.6%
7.2666666672
 
0.6%
62.972
 
0.6%
43.42
 
0.6%
3.92
 
0.6%
61.332
 
0.6%
60.032
 
0.6%
62.432
 
0.6%
Other values (316)325
93.9%
ValueCountFrequency (%)
0.531
0.3%
0.81
0.3%
1.031
0.3%
1.171
0.3%
1.371
0.3%
1.631
0.3%
2.1333333332
0.6%
2.271
0.3%
2.51
0.3%
2.71
0.3%
ValueCountFrequency (%)
87.371
0.3%
86.831
0.3%
86.271
0.3%
85.932
0.6%
84.331
0.3%
83.41
0.3%
82.931
0.3%
82.531
0.3%
81.671
0.3%
77.831
0.3%

OS_Months
Real number (ℝ)

High correlation 

Distinct321
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.032023
Minimum0.53
Maximum88.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:11.209677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.53
5-th percentile7.5775
Q126.54
median40.115
Q357.865
95-th percentile74.9575
Maximum88.63
Range88.1
Interquartile range (IQR)31.325

Descriptive statistics

Standard deviation20.548568
Coefficient of variation (CV)0.50079343
Kurtosis-0.63872963
Mean41.032023
Median Absolute Deviation (MAD)14.7
Skewness0.10925279
Sum14197.08
Variance422.24363
MonotonicityNot monotonic
2025-11-03T00:21:11.236747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.733
 
0.9%
43.333
 
0.9%
21.633
 
0.9%
262
 
0.6%
42.272
 
0.6%
19.72
 
0.6%
43.42
 
0.6%
58.332
 
0.6%
60.032
 
0.6%
62.972
 
0.6%
Other values (311)323
93.4%
ValueCountFrequency (%)
0.531
0.3%
0.81
0.3%
1.031
0.3%
1.171
0.3%
1.371
0.3%
1.631
0.3%
2.271
0.3%
2.971
0.3%
3.61
0.3%
4.171
0.3%
ValueCountFrequency (%)
88.631
0.3%
87.371
0.3%
86.831
0.3%
86.271
0.3%
85.932
0.6%
84.331
0.3%
83.41
0.3%
82.931
0.3%
82.531
0.3%
81.671
0.3%

Visiting_Staff
Real number (ℝ)

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9942197
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-03T00:21:11.256472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9080215
Coefficient of variation (CV)0.63723499
Kurtosis-1.1608759
Mean2.9942197
Median Absolute Deviation (MAD)2
Skewness0.41122301
Sum1036
Variance3.6405462
MonotonicityNot monotonic
2025-11-03T00:21:11.272791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1127
36.7%
561
17.6%
443
 
12.4%
341
 
11.8%
237
 
10.7%
624
 
6.9%
713
 
3.8%
ValueCountFrequency (%)
1127
36.7%
237
 
10.7%
341
 
11.8%
443
 
12.4%
561
17.6%
624
 
6.9%
713
 
3.8%
ValueCountFrequency (%)
713
 
3.8%
624
 
6.9%
561
17.6%
443
 
12.4%
341
 
11.8%
237
 
10.7%
1127
36.7%

Interactions

2025-11-03T00:21:09.140726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:04.806386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:05.224628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:05.535376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:05.838017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:06.122749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:06.419050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:06.697141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:06.988437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:07.266119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-03T00:21:07.566604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-03T00:21:09.118903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-03T00:21:11.303612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AJCC_SubstageAgeBMICEA_PreOpChart_NoDFS_MonthsDeathDeath_CauseDifferentiationDx_YearECOGHistologyLNRLN_PositiveLN_TotalLVILog_CEA_PreOpMSI_StatusMucinous_AnyMucinous_Gt_50OS_MonthsOp_ProcedurePNIPatient_IDPreOp_AlbuminRecurrenceRecurrence_TypeSexTumor_DepositsTumor_LocationTumor_Location_GroupTumor_Size_cmVisiting_StaffpN_StagepT_Stage
AJCC_Substage1.0000.1440.0000.0930.0000.1850.2470.2270.1460.0000.0380.1170.4290.5630.0150.2150.1650.0000.0000.0590.1190.0590.1750.0700.1830.3000.0000.0000.0790.1370.1270.3820.0000.5970.718
Age0.1441.000-0.166-0.012-0.240-0.2250.3360.1900.0850.1150.3180.0700.0900.018-0.2250.000-0.0120.0000.1440.159-0.2820.1210.000-0.240-0.3830.0000.0000.0650.000-0.0630.1100.0640.0740.0000.095
BMI0.000-0.1661.0000.0140.0360.1800.0860.0570.0000.0000.0810.000-0.092-0.093-0.0530.0130.0140.1080.0000.0000.2040.0000.1310.0360.0920.0000.0000.0930.1450.0190.0810.020-0.0100.1010.000
CEA_PreOp0.093-0.0120.0141.0000.1750.4010.0140.0580.0370.0150.0000.000-0.131-0.140-0.0170.0081.0000.1740.0000.0000.1650.0000.0000.1750.0420.0001.0000.0350.0000.0910.000-0.105-0.0370.0440.129
Chart_No0.000-0.2400.0360.1751.000-0.0690.1370.1170.0500.2490.2040.086-0.0250.0070.0840.0000.1750.0000.0000.073-0.0830.0000.1381.0000.0530.1290.3450.1710.0130.0710.0910.141-0.1710.1330.038
DFS_Months0.185-0.2250.1800.401-0.0691.0000.6720.3490.0160.4870.1170.000-0.148-0.1480.0400.1120.4010.0000.0000.0000.8710.0830.081-0.0690.2240.6100.2330.0000.0000.1200.150-0.194-0.1250.1530.117
Death0.2470.3360.0860.0140.1370.6721.0000.9890.1390.0000.3260.0320.3060.2410.0000.0930.3840.0550.0000.0000.5610.2130.0470.1210.3310.5470.0000.0000.0000.1930.1840.1220.1690.2760.182
Death_Cause0.2270.1900.0570.0580.1170.3490.9891.0000.0280.0000.2750.0000.2430.1450.1620.0940.2350.0790.0000.0000.2860.1280.0000.1430.1760.6520.0000.0000.0000.1720.2510.0170.0800.2290.126
Differentiation0.1460.0850.0000.0370.0500.0160.1390.0281.0000.0360.1310.3200.0700.0830.0000.0810.0000.2550.3140.4440.0820.5080.0000.0890.1580.2010.0000.0000.0450.0650.1360.2550.0000.1380.056
Dx_Year0.0000.1150.0000.0150.2490.4870.0000.0000.0361.0000.0590.0000.0190.0000.0000.1490.1370.0000.0000.0000.5020.1540.0000.5040.0000.1500.0560.0000.0000.0560.0000.0490.1410.1240.000
ECOG0.0380.3180.0810.0000.2040.1170.3260.2750.1310.0591.0000.1380.1580.0510.0000.1470.0000.0000.1110.1730.1410.2190.0000.1530.2330.1360.0000.0670.0000.0000.1150.1160.4110.0000.085
Histology0.1170.0700.0000.0000.0860.0000.0320.0000.3200.0000.1381.0000.2260.1630.0000.0000.0000.0680.8060.7480.0000.1790.0000.0000.1350.0750.0000.0370.0000.0600.0930.2730.1280.1630.000
LNR0.4290.090-0.092-0.131-0.025-0.1480.3060.2430.0700.0190.1580.2261.0000.927-0.2100.175-0.1310.0310.1340.035-0.1250.0000.106-0.0250.0060.2790.0000.0000.220-0.0690.0000.0780.0800.7900.063
LN_Positive0.5630.018-0.093-0.1400.007-0.1480.2410.1450.0830.0000.0510.1630.9271.0000.1420.177-0.1400.0000.1010.000-0.1160.0000.1830.007-0.0380.2450.0000.0000.118-0.1010.0000.1630.0320.6960.137
LN_Total0.015-0.225-0.053-0.0170.0840.0400.0000.1620.0000.0000.0000.000-0.2100.1421.0000.035-0.0170.0770.0000.0000.0450.0000.0000.084-0.0690.0000.0470.0980.000-0.0700.1240.226-0.1100.0000.099
LVI0.2150.0000.0130.0080.0000.1120.0930.0940.0810.1490.1470.0000.1750.1770.0351.0000.0750.0000.0140.0600.0790.0000.1420.0640.0730.1570.0000.0160.0790.0860.0000.0710.0000.2330.163
Log_CEA_PreOp0.165-0.0120.0141.0000.1750.4010.3840.2350.0000.1370.0000.000-0.131-0.140-0.0170.0751.0000.1620.0000.0000.1650.1080.0560.1750.0420.7591.0000.1580.0000.0910.036-0.105-0.0370.1110.147
MSI_Status0.0000.0000.1080.1740.0000.0000.0550.0790.2550.0000.0000.0680.0310.0000.0770.0000.1621.0000.2120.1370.0000.3060.0000.0000.1090.0000.0610.0860.0000.3230.2390.3860.0000.1300.072
Mucinous_Any0.0000.1440.0000.0000.0000.0000.0000.0000.3140.0000.1110.8060.1340.1010.0000.0140.0000.2121.0000.7080.0000.2390.0000.0000.1830.0000.0000.0000.0000.2050.1500.2200.1120.1500.000
Mucinous_Gt_500.0590.1590.0000.0000.0730.0000.0000.0000.4440.0000.1730.7480.0350.0000.0000.0600.0000.1370.7081.0000.0000.2500.0000.0000.2150.0000.0000.0000.0000.1790.1210.1960.1720.1020.079
OS_Months0.119-0.2820.2040.165-0.0830.8710.5610.2860.0820.5020.1410.000-0.125-0.1160.0450.0790.1650.0000.0000.0001.0000.1240.000-0.0830.2660.2520.0000.0770.0000.0920.067-0.190-0.1700.2150.037
Op_Procedure0.0590.1210.0000.0000.0000.0830.2130.1280.5080.1540.2190.1790.0000.0000.0000.0000.1080.3060.2390.2500.1241.0000.0570.0660.0820.0790.0000.0720.0000.6140.9110.2050.1670.0940.131
PNI0.1750.0000.1310.0000.1380.0810.0470.0000.0000.0000.0000.0000.1060.1830.0000.1420.0560.0000.0000.0000.0000.0571.0000.0770.0000.2120.0640.0000.0000.1540.1480.1560.0540.1670.115
Patient_ID0.070-0.2400.0360.1751.000-0.0690.1210.1430.0890.5040.1530.000-0.0250.0070.0840.0640.1750.0000.0000.000-0.0830.0660.0771.0000.0530.2010.4870.1850.0000.0710.0000.141-0.1710.1830.048
PreOp_Albumin0.183-0.3830.0920.0420.0530.2240.3310.1760.1580.0000.2330.1350.006-0.038-0.0690.0730.0420.1090.1830.2150.2660.0820.0000.0531.0000.1230.0000.0590.0210.2230.223-0.391-0.2450.0000.199
Recurrence0.3000.0000.0000.0000.1290.6100.5470.6520.2010.1500.1360.0750.2790.2450.0000.1570.7590.0000.0000.0000.2520.0790.2120.2010.1231.0001.0000.0000.0000.1550.0860.1070.0000.2570.234
Recurrence_Type0.0000.0000.0001.0000.3450.2330.0000.0000.0000.0560.0000.0000.0000.0000.0470.0001.0000.0610.0000.0000.0000.0000.0640.4870.0001.0001.0000.1340.1440.0000.0000.3900.2490.0000.000
Sex0.0000.0650.0930.0350.1710.0000.0000.0000.0000.0000.0670.0370.0000.0000.0980.0160.1580.0860.0000.0000.0770.0720.0000.1850.0590.0000.1341.0000.0310.0950.0960.1430.0320.0000.135
Tumor_Deposits0.0790.0000.1450.0000.0130.0000.0000.0000.0450.0000.0000.0000.2200.1180.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.1440.0311.0000.0000.0070.0000.0001.0000.000
Tumor_Location0.137-0.0630.0190.0910.0710.1200.1930.1720.0650.0560.0000.060-0.069-0.101-0.0700.0860.0910.3230.2050.1790.0920.6140.1540.0710.2230.1550.0000.0950.0001.0000.991-0.3310.0020.1480.038
Tumor_Location_Group0.1270.1100.0810.0000.0910.1500.1840.2510.1360.0000.1150.0930.0000.0000.1240.0000.0360.2390.1500.1210.0670.9110.1480.0000.2230.0860.0000.0960.0070.9911.0000.3060.0000.0000.153
Tumor_Size_cm0.3820.0640.020-0.1050.141-0.1940.1220.0170.2550.0490.1160.2730.0780.1630.2260.071-0.1050.3860.2200.196-0.1900.2050.1560.141-0.3910.1070.3900.1430.000-0.3310.3061.0000.0770.1040.420
Visiting_Staff0.0000.074-0.010-0.037-0.171-0.1250.1690.0800.0000.1410.4110.1280.0800.032-0.1100.000-0.0370.0000.1120.172-0.1700.1670.054-0.171-0.2450.0000.2490.0320.0000.0020.0000.0771.0000.0000.000
pN_Stage0.5970.0000.1010.0440.1330.1530.2760.2290.1380.1240.0000.1630.7900.6960.0000.2330.1110.1300.1500.1020.2150.0940.1670.1830.0000.2570.0000.0001.0000.1480.0000.1040.0001.0000.108
pT_Stage0.7180.0950.0000.1290.0380.1170.1820.1260.0560.0000.0850.0000.0630.1370.0990.1630.1470.0720.0000.0790.0370.1310.1150.0480.1990.2340.0000.1350.0000.0380.1530.4200.0000.1081.000

Missing values

2025-11-03T00:21:09.460546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-03T00:21:09.520818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-03T00:21:09.581099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Patient_IDChart_NoDx_DateDx_YearAgeSexBMIECOGTumor_LocationTumor_Location_GrouppT_StagepN_StageAJCC_SubstageLN_TotalLN_PositiveLNRHistologyDifferentiationLVIPNITumor_DepositsMucinous_Gt_50Mucinous_AnySignet_RingMSI_StatusTumor_Size_cmCEA_PreOpLog_CEA_PreOpRadical_Op_DateOp_ProcedurePreOp_AlbuminLast_FU_DateRecurrenceRecurrence_DateRecurrence_TypeDeathDeath_CauseDFS_MonthsOS_MonthsVisiting_Staff
011708322017-10-16201777224.242.0414A2B3C17120.705882231.01.0011NaNMSS3.50.50.4054652017-10-20Laparoscopic right hemicolectomy3.62020-03-1512019-04-08Locoregional1317.83333329.574
121907832017-06-20201782122.231.07231B3B3420.058824120.00.0000NaNMSS4.02.11.1314022017-07-07Anterior resectionNaN2024-03-200NaTNaN0082.53000082.532
233356152021-05-15202171130.121.0723NaN3B1500.000000111.00.0100NaNMSS2.62.11.1314022021-06-07Laparoscopic anterior resection3.32024-05-010NaTNaN0036.07000036.071
344581732021-11-04202187225.111.0214A1B3B1730.176471130.01.0001NaNMSI-H9.22.11.1314022021-11-22Laparoscopic right hemicolectomy3.82024-02-290NaTNaN0028.23000028.231
455367102020-07-17202090123.392.07231B3B1830.166667241.00.0011NaNMSS7.02.11.1314022020-07-24Laparoscopic anterior resection3.22020-08-190NaTNaN111.1700001.175
565456202021-07-29202187120.731.0214A1B3B2320.086957121.00.0000NaNMSS7.20.50.4054652021-08-16Laparoscopic right hemicolectomy3.52023-06-2612023-03-13Distant1119.13333323.471
676575892017-10-16201771218.890.07211B3A3430.088235120.00.0000NaNMSS0.92.21.1631512017-11-30Laparoscopic anterior resection3.62019-09-110NaTNaN1323.17000023.173
787068652018-11-26201864129.94NaN7231B3B2230.136364120.00.0000NaNMSS3.22.21.1631512018-12-18Laparoscopic anterior resectionNaN2024-05-020NaTNaN0066.13000066.132
897900782020-06-19202083222.032.0724B1B3C2120.095238121.00.0000NaNMSS3.60.50.4054652020-06-19Laparoscopic anterior resection3.52024-05-0312023-06-06Distant0036.06666747.734
9108263022018-09-04201884232.51NaN4131B3B1320.153846130.00.0000NaNMSI-H6.50.50.4054652018-09-26Laparoscopic left hemicolectomy2.72019-01-0712018-12-22Distant112.9000004.175
Patient_IDChart_NoDx_DateDx_YearAgeSexBMIECOGTumor_LocationTumor_Location_GrouppT_StagepN_StageAJCC_SubstageLN_TotalLN_PositiveLNRHistologyDifferentiationLVIPNITumor_DepositsMucinous_Gt_50Mucinous_AnySignet_RingMSI_StatusTumor_Size_cmCEA_PreOpLog_CEA_PreOpRadical_Op_DateOp_ProcedurePreOp_AlbuminLast_FU_DateRecurrenceRecurrence_DateRecurrence_TypeDeathDeath_CauseDFS_MonthsOS_MonthsVisiting_Staff
336337190705102021-10-12202139236.270.0624ANaN3C1240.333333120.01.0000NaNMSI-H5.42.11.1314022021-10-27Laparoscopic left hemicolectomyNaN2023-05-2612021-12-30Distant112.13333319.705
337338191148862021-10-06202188120.731.07232B3C1870.388889121.00.0000NaNMSS3.26.82.0541242021-10-28Laparoscopic anterior resectionNaN2024-04-080NaTNaN0030.70000030.703
338339191273342021-09-22202143219.801.07231B3B3330.090909121.00.0000NaNMSS3.02.11.1314022021-10-15Anterior resectionNaN2024-04-160NaTNaN0031.23000031.234
339340191618212021-10-11202189127.611.0214ANaN3B1600.000000120.00.0100NaNMSS6.54.61.7227672021-10-25Laparoscopic right hemicolectomyNaN2023-09-250NaTNaN1923.80000023.801
340341192197062021-11-26202154127.241.08231B3B1530.200000120.00.0000NaNMSS4.74.31.6677072021-12-13Laparoscopic low anterior resection4.62024-04-160NaTNaN0029.07000029.071
341342192344252021-11-23202158221.721.0214A1A3B2410.041667131.00.0000NaNMSS3.52.11.1314022021-12-03Laparoscopic right hemicolectomy3.62023-06-0712022-06-16Distant116.50000018.701
342343192449632021-11-25202155122.741.08221A3A2210.045455120.00.0000NaNMSS2.91.30.8329092022-01-03Laparoscopic low anterior resectionNaN2024-04-120NaTNaN0028.97000028.971
343344192778282021-11-29202162125.101.07221A3A2210.045455121.00.0000NaNMSS4.43.01.3862942021-12-20Laparoscopic anterior resection4.52021-12-300NaTNaN001.0300001.031
344345193322422022-01-04202160225.351.0723NaN3B2000.000000120.00.0100NaNMSS4.612.92.6318892022-01-25Laparoscopic anterior resection3.22024-04-110NaTNaN0027.83000027.836
345346193505952021-12-15202150219.330.0824A1A3B1410.071429120.00.0000NaNMSS5.21.10.7419372022-02-04Laparoscopic low anterior resection4.52024-02-210NaTNaN0026.60000026.603